Systems and methods for classifying sheets of a printing device

ABSTRACT

A method comprises obtaining a training dataset, the training dataset comprising sheet passage interval information associated with conveyance of a plurality of sheets in a sheet conveyance path of a training sheet processing apparatus, at least a portion of the sheets of the plurality of sheets having a known sheet classification of a set of sheet classifications; obtaining sheet timing data associated with the classifying, the sheet timing data comprising sheet passage interval information associated with conveyance of a predetermined number of sheets in the sheet conveyance path of a deployed sheet processing apparatus; classifying, using a machine learning model and the training dataset and the sheet timing data, for each interval window of a plurality of different interval windows, a predetermined number of sheets in a sheet conveyance path of a deployed sheet processing apparatus as a particular classification of the set of sheet classifications; and adjusting, based on the classifying, one or more operational parameters of a deployed sheet processing apparatus.

BACKGROUND

Copy machines, printers, fax machines, scanners, multi-function devices,and other image forming apparatuses are common devices in business andhomes. These image forming apparatuses and other sheet processingapparatuses may perform differently based on the type of paper in theimage forming apparatus. Some image forming apparatuses havefunctionality to ask a user to provide information regarding the type ofpaper. However, users may forget to enter the information, fail tochange the information when paper is changed, and/or otherwise fail toprovide the correct information to the image forming apparatus.

SUMMARY

The following implementations and aspects thereof are described andillustrated in conjunction with systems, tools, and methods that aremeant to be exemplary and illustrative, not necessarily limiting inscope. In various implementations one or more of the above-describedproblems have been addressed, while other implementations are directedto other improvements.

Various embodiments of the present disclosure include systems, methods,and non-transitory computer readable media configured to obtain atraining dataset, the training dataset comprising sheet passage intervalinformation associated with conveyance of a plurality of sheets in asheet conveyance path of a training sheet processing apparatus, at leasta portion of the sheets of the plurality of sheets having a known sheetclassification of a set of sheet classifications. Obtaining sheet timingdata associated with the classifying, the sheet timing data comprisingsheet passage interval information associated with conveyance of apredetermined number of sheets in a sheet conveyance path of a deployedsheet processing apparatus. Classifying, using a machine learning modeland the training dataset and the sheet timing data, for each intervalwindow of a plurality of different interval windows, a predeterminednumber of sheets in a sheet conveyance path of a deployed sheetprocessing apparatus as a particular classification of the set of sheetclassifications. Adjusting, based on the classifying, one or moreoperational parameters of a deployed sheet processing apparatus.

In some embodiments, the system comprises a portion of the deployedsheet processing apparatus.

In some embodiments, the system comprises at least a portion of acentral server system coupled to the deployed sheet processing apparatusover a communication network.

In some embodiments, the one or more operational parameters of thedeployed sheet processing apparatus include any of image quality andtoner usage.

In some embodiments, the systems, methods, and non-transitory computerreadable media are further configured to obtain additional training dataassociated with the deployed sheet processing apparatus; switch theclassifying from using the training dataset to using the additionaltraining data; and classify, using the machine learning model and theadditional training data, for each interval window of a second pluralityof different second interval windows, a second predetermined number ofsheets in the sheet conveyance path of the deployed sheet processingapparatus as a second classification of a second set of sheetclassifications.

In some embodiments, the switching is performed based on obtaining athreshold amount of the sheet timing data.

In some embodiments, the second set of sheet classifications includes atleast a portion of the set of sheet classifications and at least oneadditional sheet classification received by the deployed sheetprocessing apparatus.

In some embodiments, the systems, methods, and non-transitory computerreadable media are further configured to receive an actual sheetclassification for a particular interval window of the plurality ofinterval windows; compare the actual sheet classification with theparticular classification associated with the particular interval windowof the plurality of interval windows; and determine, based on thecomparison, an accuracy of the classifying.

In some embodiments, the systems, methods, and non-transitory computerreadable media are further configured to iteratively modify any of atype or number of inputs to the machine learning model until a thresholdaccuracy level is achieved.

In some embodiments, the systems, methods, and non-transitory computerreadable media are further configured to obtain additional training dataassociated with the deployed sheet processing apparatus; switch theclassifying from using the training dataset to using the trainingdataset and the additional training data; and classify, using themachine learning model and the additional training data and the trainingdataset, for each interval window of a second plurality of differentsecond interval windows, a second predetermined number of sheets in thesheet conveyance path of the deployed sheet processing apparatus as asecond classification of a second set of sheet classifications.

In some embodiments, the present invention provides a sheet processingapparatus comprising at least one roller disposed in a sheet conveyancepath and configured to convey a plurality of sheets; a sheet sensordisposed in the sheet conveyance path and configured to detect eachsheet of the plurality of sheets as the sheet is conveyed by aparticular roller of the at least one roller and to generate sensorinformation indicative of the detection; and a controller. Thecontroller is configured to, for each sheet of a predetermined number ofsheets of the plurality of sheets conveyed by the particular roller, usethe sensor information to generate an individual sheet-conveyancedataset indicating a sheet passage interval, and store the individualsheet-conveyance dataset in data storage. The controller is furtherconfigured to, after the particular roller has conveyed thepredetermined number of sheets, calculate a summary sheet-conveyancedataset including sheet passage interval statistics for thepredetermined number of sheets, store the summary sheet-conveyancedataset in the data storage, discard the individual sheet-conveyancedatasets from the data storage, and evaluate the sheet passage intervalstatistics to determine whether to modify the predetermined number basedon the evaluation.

A data size of the summary sheet-conveyance dataset may be smaller thana data size of the individual sheet-conveyance datasets for thepredetermined number of sheets. The controller may be configured todecrease the predetermined number when the sheet passage intervalstatistics indicate that the number of sheets conveyed abnormally slowlyor fast among the predetermined number of sheets is greater than a firstthreshold value, and increase the predetermined number when the sheetpassage interval statistics indicate that the number of sheets conveyedabnormally slowly or fast among the predetermined number of sheets isless than a second threshold value that is smaller than the firstthreshold value. The controller may decrease the predetermined number bya first predetermined ratio, and increase the predetermined number by asecond predetermined ratio. The first predetermined ratio may be equalto the second predetermined ratio. The controller may decrease thepredetermined number by a first predetermined number, and increase thepredetermined number by a second predetermined number. The controllermay be further configured to detect a transmission trigger event, andtransmit one or more summary sheet-conveyance datasets stored in thedata storage through a network after detecting the transmission triggerevent. The transmission trigger event may include at least one of anevent when a predetermined scheduled time is reached, an event when auser instruction is received, and an event when a control signal tonotify an error is generated. The controller may be further configuredto detect a reset trigger event, and perform a clear operation to clearone or more individual sheet-conveyance datasets and one or more summarysheet-conveyance datasets stored in the data storage after detecting thereset trigger event. The reset trigger event may include an event whenthe particular roller is replaced with a replacement roller.

In some embodiments, the present invention provides a method comprisingconveying a plurality of sheets in a sheet conveyance path of a sheetprocessing apparatus having at least one roller; using a sheet sensordisposed in the sheet conveyance path to detect each sheet of theplurality of sheets as it is conveyed in the sheet conveyance path by aparticular roller of the at least one roller and to generate sensorinformation indicative of the detection; and for each sheet of apredetermined number of sheets of the plurality of sheets conveyed bythe particular roller: using the sensor information to generate anindividual sheet-conveyance dataset indicating a sheet passage interval;and storing the individual sheet-conveyance dataset in data storage;after the particular roller has conveyed the predetermined number ofsheets: calculating a summary sheet-conveyance dataset including sheetpassage interval statistics for the predetermined number of sheets;storing the summary sheet-conveyance dataset in the data storage;discarding the individual sheet-conveyance datasets from the datastorage; and evaluating the sheet passage interval statistics todetermine whether to modify the predetermined number based on theevaluation.

The data size of the summary sheet-conveyance dataset may be smallerthan a data size of the individual sheet-conveyance datasets for thepredetermined number of sheets. Modification of the predetermined numberbased on the evaluation may comprise: decreasing the predeterminednumber when the sheet passage interval statistics indicate that thenumber of sheets conveyed abnormally slowly or fast among thepredetermined number of sheets is greater than a first threshold value;and increasing the predetermined number when the sheet passage intervalstatistics indicate that the number of sheets conveyed abnormally slowlyor fast among the predetermined number of sheets is less than a secondthreshold value that is smaller than the first threshold value. Thepredetermined number may be decreased by a first predetermined ratio,and increased by a second predetermined ratio. The first predeterminedratio may be equal to the second predetermined ratio. The predeterminednumber may be decreased by a first predetermined number, and increasedby a second predetermined number. The method may further comprisedetecting a transmission trigger event; and after detecting thetransmission trigger event, transmitting one or more summarysheet-conveyance datasets stored in the data storage through a network.The transmission trigger event may include at least one of an event whena predetermined scheduled time is reached, an event when a userinstruction is received, and an event when a control signal to notify anerror is generated. The method may further comprise detecting a resettrigger event; and after detecting the reset trigger event, performing aclear operation to clear one or more individual sheet-conveyancedatasets and one or more summary sheet-conveyance datasets stored in thedata storage. The reset trigger event may include an event when theparticular roller is replaced with a replacement roller.

These and other advantages will become apparent to those skilled in therelevant art upon a reading of the following descriptions and a study ofthe several examples of the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a schematic example of a side view of a sheetprocessing apparatus according to some embodiments.

FIG. 1B illustrates a schematic example of a side view of the sheetprocessing apparatus when multiple sheets are drawn by a sheet feedingroller according to some embodiments.

FIG. 1C illustrates a schematic example of a side view of the sheetprocessing apparatus to explain friction of a sheet with adjacentcomponents according to some embodiments.

FIG. 2 illustrates a schematic example of a perspective view of a sheetfeeding roller according to some embodiments.

FIG. 3 illustrates a schematic example of a side view of a sheet feedingroller unit according to some embodiments.

FIG. 4 illustrates a graph of example sheet passages and sheetconveyance intervals according to some embodiments.

FIG. 5 illustrates a graph of example sheet passages and sheetconveyance intervals relative to an average sheet conveyance intervalaccording to some embodiments.

FIG. 6 illustrates a table of example sheet conveyance intervals forindividual sheets according to some embodiments.

FIG. 7 illustrates a graph of example processing times per page relativeto upper and lower thresholds for anomaly detection according to someembodiments.

FIG. 8 illustrates a table of an example number of times a sheetconveyance interval exceeded an upper threshold and a lower thresholdfor specific page ranges according to some embodiments.

FIG. 9 illustrates an example system for identifying sheet conveyanceanomalies and classifying paper according to some embodiments.

FIG. 10 illustrates an example sheet conveyance path in a sheetconveyance apparatus according to some embodiments.

FIG. 11 illustrates a flowchart of an example of a method for operatinga sheet processing apparatus according to some embodiments.

FIG. 12 illustrates a flowchart of another example of a method foroperating a sheet processing apparatus according to some embodiments.

FIG. 13 illustrates a schematic example of a system for managing a sheetprocessing apparatus according to some embodiments.

FIGS. 14A and 14B illustrate a flowchart of an example of a method foroperating a sheet processing apparatus according to some embodiments.

FIG. 15 illustrates a flowchart of an example of a method for operatinga management server according to some embodiments.

FIG. 16 illustrates a flowchart of an example of a method for operatinga client device according to some embodiments.

FIG. 17 illustrates a flowchart of an example of a method forclassifying sheets according to some embodiments.

FIG. 18 illustrates a flowchart of an example of a method forclassifying sheets according to some embodiments.

FIG. 19 illustrates a table of example sheet conveyance intervals forindividual sheets according to some embodiments.

FIG. 20 illustrates a table of example summary statistics for individualsheets according to some embodiments.

FIG. 21 illustrates a table that shows predicted and actual sheets typesincluding incorrect classifications according to some embodiments.

FIG. 22 illustrates a diagram of an example pipeline for sheetclassification according to some embodiments.

FIG. 23 illustrates an example classification of a 100 ^(th) window intopremium or economy sheets according to some embodiments.

FIG. 24 illustrates a block diagram of an example computer system uponwhich the various embodiments described herein may be implemented.

DETAILED DESCRIPTION

Some embodiments of the present disclosure are directed at systems andmethods for calculating sheet-conveyance datasets to be used fordetecting sheet conveyance anomalies in a sheet processing apparatus. Insome cases, a sheet is conveyed more slowly than designed, which maycause sheet collision with a subsequently conveyed sheet. In othercases, a sheet is conveyed more quickly than designed, which may causesheet collision with a previously conveyed sheet. A sheet collision maycause a malfunctioning of the sheet processing apparatus such as sheetjams and printing problems, and may demand significant effort toresolve. Accordingly, in some embodiments, a sheet processing apparatuscalculates sheet-conveyance datasets, and summarizes thesheet-conveyance datasets to save storage space and store more datasets.The sheet processing apparatus further transmits the sheet-conveyancedatasets to an external system, such as a server, such that the externalsystem can determine abnormal sheet conveyances based on thesheet-conveyance datasets and evaluate the sheet conveyance anomalies todetermine when to notify a user.

In some embodiments, a sheet processing apparatus includes at least oneroller, a sheet sensor, and a controller. The roller is disposed in asheet conveyance path and configured to convey a plurality of sheets.The sheet sensor is disposed in the sheet conveyance path, possiblyadjacent to the roller, and is configured to detect each sheet of theplurality of sheets as the sheet is conveyed by a particular roller ofthe at least one roller. The sheet sensor is also configured to generatesensor information indicative of the detection. In some embodiments, thecontroller is configured to, for each sheet of a predetermined number ofsheets of the plurality of sheets conveyed by the particular roller, usethe sensor information to generate an individual sheet-conveyancedataset indicating a sheet passage interval, and store the individualsheet-conveyance dataset in data storage. The data storage may be aninternal storage of the sheet processing apparatus and/or an externalstorage detachably attached to the sheet processing apparatus. Thecontroller is further configured to, after the particular roller hasconveyed the predetermined number of sheets, calculate a summarysheet-conveyance dataset including sheet passage interval statistics forthe predetermined number of sheets, store the summary sheet-conveyancedataset in the data storage, and discard the individual sheet-conveyancedatasets from the data storage. The controller may be further configuredto evaluate the sheet passage interval statistics to determine whetherto modify the predetermined number based on the evaluation.

According to a sheet processing apparatus of some embodiments, a certainnumber of individual sheet-conveyance datasets are generated, a summarysheet-conveyance dataset is calculated therefrom, and the individualsheet-conveyance datasets are discarded to secure more space of the datastorage. As a result, even if a large number of sheets are processed ina short period of time, sheet-conveyance datasets can be stored withoutlosing data, and sheet conveyance anomalies can be more reliablydetected based on the sheet-conveyance datasets.

Some embodiments of the present disclosure are directed to a system forclassifying sheets (e.g., sheets of paper) in a sheet processingapparatus. Example sheet processing apparatuses include printers,multi-function printers, facsimile machines, copiers, and scanners. Thesystem may collect the paper travel time data (e.g., sheet-conveyancedatasets, summary statistics) from a deployed sheet processingapparatus, aggregate such data, and a predict a sheet type (e.g.,economy or premium) using a machine learning technique and/or model(e.g., a supervised machine learning model). Based on the predictedclassification, the system may adjust one or more operational parametersof the sheet processing apparatus. For example, the system may adjustimage quality, enhance toner usage, prolong life of components (e.g.,printer cartridges, sensors, rollers, and/or the like).

FIG. 1A illustrates a schematic example of a side view of a sheetprocessing apparatus 100 according to some embodiments. FIG. 1Billustrates a schematic example of a side view of the sheet processingapparatus 100 when multiple sheets are drawn by a sheet feeding rolleraccording to some embodiments. FIG. 1C illustrates a schematic exampleof a side view of the sheet processing apparatus 100 to explain frictionof a sheet with adjacent components according to some embodiments. InFIG. 1B and FIG. 1C, several components of the sheet processingapparatus 100 are not depicted to simplify the illustration. In theexample, the sheet processing apparatus 100 includes a sheet feedingroller 101, a lifting panel 103, a sensor 104, a sheet holder 105 onwhich a plurality of sheets 102 is placed, and a controller 106. In someembodiments, the sheet processing apparatus 100 is a sheet feedingdevice independent of or integrated in an image forming apparatus, suchas a printer, a copier, a scanner, a facsimile, a multi-functionperipheral (MFP), and so on, and is configured to supply sheets to thesheet processing apparatus 100. In some embodiments, the sheetprocessing apparatus 100 is a finisher independent of or integrated inthe image forming apparatus, configured to receive sheets from the imageforming apparatus, and configured to perform sheet processing such asstapling, binding, folding, punching, and so on.

The sheet feeding roller 101 is intended to represent a rollerconfigured to rotate about a rotational axis for conveying sheets 102placed on the sheet holder 105, one by one. Depending upon a specificimplementation and other considerations, a surface of the sheet feedingroller 101 is formed of a material that causes frictional gripping forcewith a surface of a conveyed sheet. For example, a surface of the sheetfeeding roller 101 is formed of rubber, resin, metal, and so on. Forexample, a surface of the sheet feeding roller 101 has a rugged shape, asmooth shape, a gear shape, and so on. The sheets discussed in thepresent disclosure include paper (e.g., copier paper), transparentfilms, cardboard (e.g., post card, business card, etc.), and so on. Insome embodiments, the sheet feeding roller 101 is disposed above an endof the sheet holder 105 from which the sheets 102 are conveyed. In someembodiments, the sheet feeding roller 101 is caused to rotate in a sheetconveying direction by a motor or any other applicable actuator, inorder to convey the sheets 102. The sheet feeding roller 101 may beconfigured to rotate at a predetermined rotational speed. In someembodiments, the predetermined rotational speed may differ based onprint/scan quality settings or other settings. Rotational speed can bemonitored separately from the sheet conveyance interval.

The lifting panel 103 is intended to represent a member configured toguide sheets 102 conveyed by the sheet feeding roller 101 in the sheetconveying direction. The lifting panel 103 is inclined towards an upperdirection with respect to a surface of the sheet holder 105 on which thesheets 102 are placed. Depending upon a specific implementation andother consideration, a surface of the lifting panel 103 is formed ofapplicable materials, such as plastic, resin, metal, a combinationthereof, and so on.

The sensor 104 is intended to represent a sensor configured to detectpassage of a sheet at the position of the sensor. Depending upon aspecific implementation and other considerations, the sensor 104 mayemploy any applicable technique to detect a sheet passage. For example,the sensor 104 may include an optical sensor configured to detect thestart of a sheet and the end of a sheet based on lighting changes causedby the passage of the sheet. In another example, the sensor 104 includesa mechanical sensor configured to detect the start of a sheet and theend of a sheet based on a mechanical movement of parts caused by thepassage of the sheet. In some embodiments, the sensor 104 may beconfigured to generate a detection signal when a front end of a sheetpasses the sensor 104 and a tail end of the sheet passes the sensor 104.In some embodiments, the sensor 104 is disposed at a position adjacentto the sheet feeding roller 101, e.g., immediately before or immediatelyafter the sheet feeding roller 101. In some embodiments, the sensor 104is disposed above or below the sheet feeding roller 101.

The sheet holder 105 is configured to support the sheets 102 and allowthe sheets 102 to be conveyed by the sheet feeding roller 101. Dependingon implementation, the sheet holder 105 may be formed of any applicablematerials, such as plastic, resin, metal, and combination thereof. Insome embodiments, mechanical levers on the sheet holder 105 may beadjustably fixed to support and identify the size in the sheets. Thecontroller 106 may be capable of detecting the size of the sheets in thesheet holder 105 based on the positions of the levers. In someembodiments, sheet size can be determined based on electronic settings.In some embodiments, the sheet holder 105 is configured to move up anddown depending on weights of the sheets 102 placed thereon, such that atop sheet is at a position that can be positioned to contact the sheetfeeding roller 101 for conveyance.

The controller 106 is a hardware computing device configured to controloperations of the sheet processing apparatus 100. In some embodiments,the controller 106 controls rotation of the sheet feeding roller 101 andprocesses signals generated by and received from the sensor 104. In someembodiments, the controller 106 may control the sheet feeding roller 101to rotate at a predetermined rotational rate. In some embodiments, thecontroller 106 is configured to calculate sheet passage intervals basedon the detection signals generated by and received from the sensor 104.For example, the controller 106 calculates a sheet passage intervalbased on a time of a detection signal corresponding to detection of afront end of a sheet and a time of a detection signal corresponding todetection of a tail end of the sheet. In some embodiments, thecontroller 106 is configured to compare each sheet passage intervalagainst one or more thresholds, to determine whether the sheet has beenconveyed by the sheet feeding roller 101 within a trusted time interval.For example, the controller 106 compares a sheet passage interval with afirst threshold to determine whether conveyance of the sheet isexcessively slow, which may suggest an error in the conveyance of thesheet. In another example, the controller 106 compares a sheet passageinterval with a second threshold (lower than the first threshold) todetermine whether conveyance of the sheet is excessively fast, which mayalso suggest a different error in the conveyance of the sheet.

In some embodiments, the controller 106 may be configured to determinedeviations of sheet passage intervals of sheets across multiple sensorsin the sheet conveyance path of the sheet processing apparatus 100. Thecontroller 106 may detect sheet passage anomalies based on deviations.In some embodiments, sheet conveyance intervals may be expected to berelatively consistent across the sheet conveyance path.

Based on a number or egregiousness of anomalies, e.g., when a sheetpassage interval is greater than the first threshold (excessively slow),a friction force between the sheet feeding roller 101 and a top sheetmay be insufficient. In a case, when a kinematic friction coefficientμ01 (shown in FIG. 1C) of the friction between the sheet feeding roller101 and the top sheet is excessively low, the friction force may beinsufficient. In a case, anomalies of the kinematic friction coefficientμ01 may be caused by defects of the sheet feeding roller 101, improperquality of sheet (e.g., slippery sheet) may lead to longer sheet passageinterval. Similarly, when a sheet passage interval is smaller than thesecond threshold (excessively fast), the sheet feeding roller 101 may bemalfunctioning, a sheet 108 underneath a top sheet may be conveyedtogether with the top sheet (shown in FIG. 1B), and so on. In a case,when a kinematic friction coefficient μ02 (shown in FIG. 1C) of thefriction between the top sheet and the sheet 108 underneath the topsheet is excessively high, the friction force may be excessively high.In a case, anomalies of the kinematic friction coefficient μ02 may becaused by improper quality and/or conditions of sheets (e.g., wetsheets, jammed sheets, etc.) may lead to the companied sheet conveyance.

In some embodiments, notifications may be generated when there is aspecific count of anomalies, when an anomaly is significantly outside ofthe expected range. Anomaly count may be based on the significance ofthe anomalies, e.g., anomalies may be weighted based on how far outsidethe trusted range they are, e.g., how many standards of deviation theyare outside the trusted range.

In some embodiments, the controller 106 may be configured todifferentiate or adjust the first threshold and/or the second thresholdbased on various applicable criteria. For example, the controller 106differentiates the first threshold and/or the second threshold based onat least one of sheet type, sheet size (length) in a sheet conveyingdirection, rotational rate of the sheet feeding roller 101, etc. In someembodiments, the controller 106 may increase the first threshold and/orthe second threshold as a surface roughness of sheet (e.g., arithmeticalmean deviation) becomes smaller. In some embodiments, the controller 106may increase the first threshold and/or the second threshold when alength of sheet in the sheet conveying direction is longer. In someembodiments, the controller 106 may increase the first threshold and/orthe second threshold as the rotational rate of the sheet feeding roller101 is slowed. In some embodiments, as image forming quality (e.g., DPIvalue) becomes higher, the rotational rate of the sheet feeding roller101 may become slower.

In some embodiments, the controller 106 may differentiate the firstthreshold and/or the second threshold based on a number of sheetpassages within a certain duration of time. The controller 106 maymodify the first threshold and/or the second threshold for a first groupof sheets (e.g., first several number of sheets) relative to a secondgroup of sheets (e.g., second several number of sheets) passed after thefirst group of sheets, and/or may modify the first threshold and/or thesecond threshold for the second group of sheets than the first group ofsheets. This differentiation may be based on expectation of greaterinterval fluctuation as the cumulative number increases.

In another example, the controller 106 differentiates the firstthreshold and/or the second threshold based on at least one of ageographical region at which the sheet processing apparatus 100 islocated, a model of an image processing device (e.g., printing device,scanning device, etc.) incorporating or coupled to the sheet processingapparatus 100, use purpose (e.g., business, home, etc.) of the sheetprocessing apparatus 100, and recommended values for the sheetprocessing apparatus 100 determined based on the various applicablecriteria. In some embodiments, the controller 106 may increase the firstthreshold and/or the second threshold based on an average humidity ofthe geographical region (e.g., city, county, etc.) at which the sheetprocessing apparatus 100 is located. In some embodiments, the controller106 may increase the first threshold and/or the second threshold basedon environmental conditions (e.g., humidity, temperature, UV amount,etc.) of a more specific location at which the sheet processingapparatus 100 is located, such as a building, a floor in a building, aroom in a building, and an air conditioning zone in a building.

In some embodiments, the controller 106 may be configured to count thenumber of times the sheet passage interval is greater than the firstthreshold (hereinafter “first threshold count”) and store the firstthreshold count in data storage included in or coupled to the controller106. Similarly, in some embodiments, in processing the detection signalsgenerated by the sensor 104, the controller 106 is configured to countthe number of times the sheet passage interval is smaller than thesecond threshold (hereinafter “second threshold count”) and store thesecond threshold count in data storage included in or coupled to thecontroller 106. The first threshold count and/or the second thresholdcount may suggest a degree of a sheet conveyance anomaly. For example,when the first threshold count is 5, the sheet conveyance anomaly islikely more serious than when the first threshold count is 2. The datastorage may have a limited capacity. Since storing the calculated sheetpassage intervals for each sheet passage may require a large datacapacity, the calculated sheet passage interval may not be stored in thedata storage.

In some embodiments, the controller 106 may be configured to count thefirst threshold count and/or the second threshold count within aparameter. The parameter may be an applicable duration of time. In someimplementations, the certain duration of time is a past predeterminedperiod of time sliding according to time passage (e.g., sliding window).For example, the past predetermined period of time may be the pastseveral hours, the past day (24 hours), the past week, the past month,the past 30 days, and so on. In some embodiments, the parameter may bebased on usage of the sheet processing apparatus. For example, theparameter may be based on the last predetermined number of sheets (e.g.,300 sheets, 500 sheets, etc.) conveyed by the sheet feeding roller 101.In such a case, the controller 106 may be looking for a certain rate ofanomalous passage times over a rolling sheet count or a certain numberof anomalous passage intervals over a period of time.

In some embodiments, the controller 106 may be configured to determinewhether or not the first threshold count and/or the second thresholdcount meets an alert condition. In some embodiments, an alert conditionincludes one or more of a condition that the first number of timesexceeds a first alert threshold, a condition that the second thresholdcount exceeds a second alert threshold, and a condition that a sum ofthe first and second threshold counts exceeds a third alert threshold.

In some embodiments, the controller 106 may be configured todifferentiate or adjust the first alert threshold, the second alertthreshold, and/or the third alert threshold based on various applicablecriteria. For example, the controller 106 may differentiate the firstalert threshold, the second alert threshold, and/or the third alertthreshold based on an image forming quality (e.g., DPI value) and/oruser setting. In a specific implementation, the controller 106 sets oneor more of the first, second, and third alert thresholds to be lower asthe image forming quality becomes higher.

In another example, the controller 106 differentiates the first alertthreshold and/or the second alert threshold based on at least one of thegeographical region, the model of the image processing device, the usepurpose, and recommended values, in a similar manner as the criteriathat can be employed for the first threshold and/or the second thresholddescribed above.

In some embodiments, the controller 106 may be configured to compare thefirst threshold count and/or the second threshold count obtained atdifferent periods, and determine whether or not an alert condition hasbeen met based on the comparison. For example, the controller 106compares the first threshold count and/or the second threshold countobtained one day before (past 24 hours) with the first threshold countand/or the second threshold count obtained two days before (past 24-48hours). In such a case, the alert condition may be that an increaseamount of the first threshold count and/or the second threshold countexceeds a predetermined threshold, or that an increase rate of the firstthreshold count and/or the second threshold count exceeds apredetermined threshold. In another example, the controller 106 comparesthe first threshold counts and/or the second threshold counts obtainedin the past 24 hours at two different time points (e.g., noon andmidnight).

In some embodiments, the controller 106 may be configured to generate acontrol signal upon an alert condition being met, and output anotification upon an alert condition being met. In some embodiments, thecontrol signal causes output of a notification, which may include thefirst threshold count and/or the second threshold count to be presentedto users. For example, when the controller 106 causes the firstthreshold count and/or the second threshold count to be displayed on adisplay device included in or coupled to the sheet processing apparatus100, the controller 106 outputs the first threshold count and/or thesecond threshold count to the display device. In some embodiments, thenotification includes a message to a user, which may notify a currentstate of sheet conveyance, a proposal to perform maintenance, an alertmessage that image processing may not be properly performed, and so on.

In some embodiments, the control signal causes change of an operationalmode of the sheet processing apparatus 100 upon the alert conditionbeing met. In some embodiments, the operational modes include a normalsheet conveyance mode in which a sheet conveyance is performed normallyand an error sheet conveyance mode in which a sheet conveyance may beperformed at a reduced sheet conveyance speed with respect to the normalmode or may not be performed.

In some embodiments, the control signal may be differentiated dependingon the first threshold count and the second threshold count. Forexample, if the first threshold count is greater than the secondthreshold count, the controller 106 may generate a first control signal,which may cause generation of a message indicating that the sheetfeeding roller 101 needs to be checked. For example, if the secondthreshold count is greater than the first threshold count, thecontroller 106 may generate a second control signal, which may causegeneration of a message indicating that quality and/or conditions ofsheets 102 needs to be checked. For example, if the first thresholdcount is equal to the second threshold count, the controller 106 maygenerate a third control signal, which may cause generation of a messageindicating that setting for a rotational rate of the sheet feedingroller 101 and/or placement of the sheets 102 needs to be checked.

FIG. 2 illustrates a schematic example of a perspective view of a sheetfeeding apparatus 200 according to some embodiments. In the example ofthe sheet processing apparatus 200 shown in FIG. 2, a sheet feedingroller 201 includes a sheet contact surface 211 and a rotational axis212. In some embodiments, the sheet feeding roller 201 corresponds tothe sheet feeding roller 101 in FIG. 1A.

The sheet contact surface 211 is intended to represent a surface of thesheet feeding apparatus 200 designed to be in contact with a sheet 202conveyed by the sheet feeding roller 201. In some embodiments, the sheetcontact surface 211 has a rugged surface so as to ensure sufficientfriction between the sheet contact surface 211 and a surface of thesheet 202. Depending upon a specific implementation and otherconsiderations, the sheet contact surface 211 is formed of applicablematerials, such as rubber, resin, metal, combination thereof, and so on.

The rotational axis 212 is intended to represent a member coupled to amain body of the sheet feeding roller 201 including the sheet contactsurface 211. In some embodiments, the rotational axis 212 is caused torotate in a sheet conveying direction by a motor or any applicableactuator (not shown in FIG. 2) provided at an end of the rotational axis212.

Depending upon a specific implementation and other considerations, whenthe sheet 202 is not pressed against the sheet contact surface 211 withsufficient force, the sheet 202 may not be properly conveyed or may beconveyed at a speed slower than intended.

FIG. 3 illustrates a schematic example of a side view of a sheet feedingapparatus with a sheet feeding roller unit 300 according to someembodiments.

In the example of the sheet processing apparatus shown in FIG. 3, asheet feeding roller unit 300 includes a sheet pickup roller 302, asheet feeding roller 303, and a sheet separation roller 304. In someembodiments, the sheet pickup roller 302 and/or the sheet feeding roller303 corresponds to the sheet feeding roller 101 in FIG. 1A.

The sheet pickup roller 302 is intended to represent a roller configuredto pick up a sheet 301 that comes in contact with the sheet pickuproller 302. In some embodiments, the sheet pickup roller 302 ispositioned above a sheet holder (not shown) so as to be in contact witha top sheet on the sheet holder.

The sheet feeding roller 303 is intended to represent a rollerconfigured to convey a sheet 301 picked up by the sheet pickup roller302. In some embodiments, a rotational direction of the sheet feedingroller 303 is the same as that of the sheet pickup roller 302. In someembodiments, the sheet pickup roller 302 and the sheet feeding roller303 are mechanically coupled such that rotations of the sheet pickuproller 302 and the sheet feeding roller 303 coincide. In a specificimplementation, conveyance speeds (e.g., radius x rotational rate) ofthe sheet pickup roller 302 and the sheet feeding roller 303 are thesame.

The sheet separation roller 304 is intended to represent a rollerconfigured to separate the top sheet, such that only the top sheet isconveyed by the sheet feeding roller 303. In some embodiments, arotational rate and/or a conveyance speed of the sheet separation roller304 may be different (e.g., slower) from a rotational rate and/or aconveyance speed of the sheet feeding roller 303. Also, depending upon aspecific implementation and other considerations, a rotational directionof the sheet separation roller 304 may be opposite to the rotationaldirection of the sheet feeding roller 303.

In some embodiments, a sensor to detect sheet passage (not shown in FIG.3) may be disposed at any applicable positions of the sheet feedingroller unit 300. In some embodiments, the sensor may be disposed betweenthe sheet pickup roller 302 and the sheet feeding roller 303. The sensormay be disposed at a position after the sheet feeding roller 303 in thesheet conveyance direction. The sensor may be configured to detect asheet conveyance anomaly caused by the sheet pickup roller 302, thesheet feeding roller 303 and/or the sheet separation roller 304.

FIG. 4 illustrates a graph 400 of sheet passage relative to sheetconveyance interval according to some embodiments. The vertical axisshown in FIG. 4 indicates a sheet conveyance interval (millisecond), andthe horizontal axis indicates a sheet passage that has been performedduring a certain duration of time. The graph shown in FIG. 4 isgenerated from sheet passage signals generated by one sensor disposed ina sheet conveyance path, e.g., a position adjacent to a sheet feedingroller.

As shown in FIG. 4, the fluctuation of the sheet conveyance interval issmall for the earlier several pages, but is significant for the laterpages. This result may be caused by defect and/or degradation of one ormore rollers.

FIG. 5 illustrates a graph 500 of a sheet conveyance interval shiftedrelative to an average sheet conveyance interval according to someembodiments. The vertical axis indicates a sheet conveyance interval(millisecond) relative to a zero-mean, and the horizontal axis indicatesa sheet passage that has been performed during a certain parameter(duration of time or usage).

As shown in FIG. 5, a fluctuation of the sheet conveyance interval issmall for the earlier several pages, but becomes larger for the laterpages. As a result, a standard deviation of the sheet conveyanceinterval for a first group of pages (i.e., δ0) is smaller than that fora second group of pages after the first group of pages (i.e., δ1). Insome embodiments, the standard deviation can be used to determine thefirst threshold and/or the second threshold. In some embodiments, thestandard deviation may be recalculated dynamically for further sheetinterval evaluation. In some embodiments, as a standard deviationincreases, the first threshold may be increased. As a standard deviationdecreases, the second threshold may be decreased. In some embodiments,the thresholds may be static, set by the manufacturer, and/or modifiedby the user.

FIG. 6 illustrates a chart 600 of example sheet conveyance intervals forN pages according to some embodiments. As shown in FIG. 6, a sheetconveyance interval may become excessively large or small as the numberof sheet passages increases. As shown, the sheet conveyance intervalsfor pages 1, 2, and 3 are within a normal range (e.g., 50-150 ms), andthe sheet conveyance interval for page N is outside of the normal range.

FIG. 7 illustrates a graph 700 of sheet conveyance intervals relative tothe first and second thresholds for anomaly detection according to someembodiments. The vertical axis indicates sheet conveyance interval, andthe horizontal axis indicates the sheet passage that has occurred duringa certain duration of time or number of sheet passages.

As shown in FIG. 7, two horizontal lines are drawn at TH1 and TH2. Theline at TH1 corresponds to the first threshold above which a controller(e.g., the controller 106 in FIG. 1A) counts an anomaly, and the line atTH2 corresponds to a second threshold below which the controller countsan anomaly. In the example of FIGS. 7, P6 and P9 are below the secondthreshold TH2, and P8 is above the first threshold TH1, respectively.

FIG. 8 illustrates a first threshold count CA1 indicating the number ofsheet conveyance intervals that exceeded the upper threshold and asecond threshold count CA2 indicating the number of sheet conveyanceintervals that were lower than the lower threshold for a time duration,a page range, a number of pages, etc., according to some embodiments. Asshown in FIG. 8, the first threshold count CA1 (exceeding the upperthreshold TH1) and the second threshold count CA2 (below the lowerthreshold TH2) are totaled for each of multiple groups of pages (e.g.,P1-P5 and P6-P9). The upper threshold and/or the lower threshold may ormay not be the same between P1-P5 and P6-P9. As shown, page range P1-P5had no anomalous passage intervals below the lower threshold TH2 and noanomalous passage intervals above the upper threshold TH1. Page rangeP6-P9 had two anomalous passage intervals below the lower threshold TH2and one anomalous passage interval above the upper threshold TH1. Pagerange P10-P100 had eight anomalous passage intervals below the lowerthreshold TH2 and fifteen anomalous passage interval above the upperthreshold TH1.

In a situation where the second threshold count CA2 is (excessively)greater than the first threshold count CA1 (CA2>CA1), it may suggestthat a sheet feeding roller has excessively degraded (worn out) and notthat the sheets are in improper quality or condition. In a situationwhere the first threshold count CA1 is (excessively) greater than thesecond threshold count CA2 (CA1>CA2), it may suggest that sheets are inimproper quality or condition and not that the sheet feeding roller hasexcessively degraded (worn out). In a situation where the firstthreshold count CA1 is equal to or roughly equal to (e.g., +/−5%, 10%,15%) the second threshold count CA2 (CA1≈CA2), it may suggest that thesetting of a rotational rate of a sheet feeding roller and/or sheetplacement of sheets to be conveyed is improper and needs adjustment.

FIG. 9 illustrates an example system 900 for determining and notifyingsheet conveyance anomalies according to some embodiments. In the exampleof the system shown in FIG. 9, the system 900 includes an image formingapparatus 901, a server apparatus 902, and/or a mobile computing device903.

The image forming apparatus 901 is intended to represent an examplesheet processing apparatus. The image forming apparatus 901 may performimage forming operations in accordance with any applicable image formingtechnique, including electrophotographic image forming, inkjet imageforming, and so on. In some embodiments, the image forming apparatus 901includes or is coupled to at least part of a sheet processing apparatus,such as the sheet processing apparatus 100 shown in FIG. 1A. Forexample, the image forming apparatus 901 includes a sheet feeding rollerand a sensor configured to detect sheet passage as in the sheetprocessing apparatus 100 shown in FIG. 1A. In some examples, the imageforming apparatus 901 also includes a controller such as the controller106 in FIG. 1A.

The server apparatus 902 is intended to represent an apparatusconfigured to process data regarding sheet conveyance anomalies. In someembodiments, the server apparatus 902 is couplable to the image formingapparatus 901 through a wired and/or wireless connection and configuredto receive detection signals generated by a sensor included in the imageforming apparatus 901 and process the detection signals. In someembodiments, the server apparatus 902 includes a processing deviceconfigured to function as a controller such as the controller 106 inFIG. 1A.

The mobile computing device 903 is intended to represent an apparatusconfigured to receive data regarding sheet conveyance anomalies andpresent information about sheet conveyance anomalies on a displaythereof. In some embodiments, the mobile computing device 903 iscouplable to the server apparatus 902 through a wired and/or wirelessconnection and configured to generate the information about sheetconveyance anomalies based on the data received. In some embodiments,the information about sheet conveyance anomalies may include anyapplicable information, such as the first and second threshold counts,e.g., the numbers of times a sheet conveyance interval is out of a range(e.g., below and/or above thresholds), a notification of a sheetconveyance anomaly or situation involving a sheet conveyance anomaloussituation, a location (e.g., a roller) at which the sheet conveyanceanomaly is considered to have happened, a message (e.g., warning,instruction) to perform maintenance, and so on. In addition, anyapplicable network topology for providing notification of a sheetconveyance anomaly can be employed. For example, the mobile computingdevice 903 may directly receive data regarding each sheet conveyanceanomaly (e.g., detection signals) from the image forming apparatus 901.

FIG. 10 illustrates an example sheet conveyance path 1000 in an imageforming apparatus according to some embodiments. In the example of thesheet conveyance path 1000 shown in FIG. 10, the image forming apparatusincludes a first feed roller 1001, a second feed roller 1002, aphotosensitive drum 1003, a fixing roller 1004, a third feed roller1005, a fourth feed roller 1006, and a plurality of sensors 1007.

The first feed roller 1001 is intended to represent a roller configuredto introduce a sheet into the image forming apparatus from a sheetholder disposed at a bottom portion thereof. In some embodiments, thefirst feed roller 1001 corresponds to the sheet feeding roller 303 inFIG. 3.

The second feed roller 1002 is intended to represent a roller configuredto introduce a sheet into the image forming apparatus from a sheetholder (e.g., manual sheet feeder) disposed at a side portion thereof.

The photosensitive drum 1003 is intended to represent a rollerconfigured to transfer a toner image formed thereon onto a sheet passingtherethrough. Any known applicable technique can be employed for thestructure and configuration of the photosensitive drum 1003.

The fixing roller 1004 is intended to represent a roller configured tofix the toner image transferred to the sheet at the photosensitive drum1003 on the sheet, by applying heat. Any known applicable technique canbe employed for the structure and configuration of the fixing roller1004.

The third feed roller 1005 is intended to represent a roller configuredto convey a sheet conveyed from the fixing roller 1004. The sheetconveyed by the third feed roller 1005 may be conveyed to a side portionof the image forming apparatus or toward a top portion of the imageforming apparatus, depending on sheet processing setting.

The fourth feed roller 1006 is intended to represent a roller configuredto convey a sheet conveyed from the third feed roller 1005 to a sheetexit tray formed at the top portion of the image forming apparatus.

Each of the plurality of sensors 1007 is intended to represent a sensorconfigured to detect passage of a sheet, and is represented by atriangle in FIG. 10. Depending upon a specific implementation and otherconsiderations, one or more of the sensors 1007 corresponds to thesensors 104 in FIG. 1A, and detection signals from the one or more ofthe sensors 1007 can be used to detect a sheet conveyance anomaly at theposition of the one or more of the sensors 1007. In some embodiments,the sheet conveyance anomaly can inform a user of the possibility of adefect in one or more rollers included in the image forming apparatus.

In some embodiments, the controller may compare sheet conveyanceintervals at the multiple sensor positions to identify sheet intervaldeviation that may cause a malfunction (e.g., paper jam or printingerror). The positions of a sensor identifying an anomaly may indicate aproblem with the roller adjacent the sensor.

FIG. 11 illustrates a flowchart 1100 of an example of a method foroperating a sheet processing apparatus. An applicable module foroperating a sheet processing apparatus, such as the controller 106 inFIG. 1A, can perform steps of the flowchart 1100. The flowchart 1100begins at step 1102 with receiving detection signals from a sheet sensor(e.g., the sheet sensor 104 in FIG. 1A). In some embodiments, thedetection signals are received when a front end of a sheet passes thesheet sensor and a tail end of the sheet passes the sheet sensor.

The flowchart 1100 continues to step 1104 with calculating a sheetpassage interval for the sheet to pass the sheet sensor. In someembodiments, the sheet passage interval is determined based on a timeperiod from the time when a detection signal corresponding to passage ofthe front end of the sheet is received to the time when a detectionsignal corresponding to passage of the tail end of the sheet isreceived. Steps 1102 and 1104 are carried out for each of a plurality ofsheets.

The flowchart 1100 continues to step 1106 with counting the number oftimes the sheet passage interval is higher than a first threshold withina certain duration of time or within a predetermined number of sheets.In some embodiments, the first threshold may be determined based on atleast one of a type of sheet, a length of sheet in a sheet conveyingdirection, a rotational rate of a sheet feeding roller, and/or acumulative number of sheet passages in the duration of time or in thepredetermined number of sheets.

The flowchart 1100 continues to step 1108 with counting the number oftimes the sheet passage interval is lower than the second thresholdwithin the certain duration of time or within a predetermined number ofsheets. In some embodiments, the second threshold is lower than thefirst threshold and may be determined also based on at least one of thetype of sheet, the length of sheet in the sheet conveying direction, therotational rate of the sheet feeding roller, and/or the cumulativenumber of sheet passages in the duration of time or in the predeterminednumber of sheets.

The flowchart 1100 continues to step 1110 with outputting the countednumbers of sheet conveyance anomalies in the steps 1106 and 1108. Insome embodiments, the counted numbers of sheet conveyance anomalies inthe steps 1106 and 1108 are output to any applicable devices including adisplay coupled to or integrated with the sheet processing apparatus, aserver apparatus for distribution to user devices such as a mobilephone, and/or the user device. A notification may be generated when analert condition is satisfied, the alert condition being based on thecount of anomalies exceeding the first threshold, below the secondthreshold, and/or deviating from others in the sheet conveyance path.Other factors may also be part of the alert condition.

FIG. 12 illustrates a flowchart 1200 of another example of a method foroperating a sheet processing apparatus according to some embodiments. Anapplicable module for operating a sheet processing apparatus, such asthe controller 106 in FIG. 1A, can perform steps of the flowchart 1200.The flowchart 1200 begins at step 1202 with determining whether a totalthreshold count, which is a sum of a first threshold count (e.g., CA1 inFIG. 8) and a second threshold count (e.g., CA2 in FIG. 8), increasedsince the last analysis of sheet conveyance anomaly. In someembodiments, the analysis of sheet conveyance anomaly is carried out atapplicable timing, such as every predetermined duration of time or inresponse to a triggering event, such as a user command, an excessiveincrease rate of a threshold count, detection of sheet jam, detection ofsheet conveyance anomaly, and so on. When it is determined that thetotal threshold count has not increased (No in step 1202), the flowchart1200 ends.

When it is determined that the total threshold count increased (Yes instep 1202), the flowchart 1200 continues to step 1204 with determiningwhether the first threshold count is greater than the second thresholdcount.

When it is determined that the first threshold count is greater than thesecond threshold count (Yes in step 1204), the flowchart 1200 continuesto step 1206 with generating a control signal causing a first messagerecommending checking sheets, and ends the analysis. In someembodiments, the first message may specify the type of sheets to beused, orientation of the sheets, conditions of the sheets, and so on.

When it is determined that the first threshold count is not greater thanthe second threshold count (No in step 1204), the flowchart 1200continues to step 1208 with generating a control signal causing a secondmessage recommending checking sheet feeding rollers, and ends theanalysis. In some embodiments, the second message may specify the typeof sheets to be used, orientation of the sheets, conditions of thesheets, and so on.

FIG. 13 illustrates a schematic example of a system 1300 for managing asheet processing apparatus according to some embodiments. In the exampleof the system shown in FIG. 13, the system 1300 includes a network 1302,a sheet processing apparatus 1304, a management server 1306, a clientdevice 1308, a training system 1340, and a sheet classification system1350, each coupled to the network 1302.

In the example of the network system 1300 shown in FIG. 13, the network1302 may include any one or more of, for instance, the Internet, anintranet, a PAN (Personal Area Network), a LAN (Local Area Network), aWAN (Wide Area Network), a SAN (Storage Area Network), a MAN(Metropolitan Area Network), a wireless network, a cellularcommunications network, a Public Switched Telephone Network, and/orother network. In some embodiments, the wireless communication includesone or more of long-range wireless communication based on GSM, W-CDMAand/or CDMA2000 (3G), IEEE 802.16 (e.g., WiMAX, 4G LTE, etc.), IEEE802.11 (e.g., WiFi, 5G), and so on. According to variousimplementations, the components described herein may be implemented inhardware and/or software that configures hardware.

In the example of the network system 1300 shown in FIG. 13, the sheetprocessing apparatus 1304 is configured to perform sheet processing,such as printing, folding, stapling, etc. on one or more sheets conveyedtherethrough. The sheet processing apparatus 1304 may correspond to asheet processing apparatus part of which are described above in FIGS.1A-3 and/or FIG. 9. As shown, the sheet processing apparatus 1304includes a sheet sensor 1310, a control engine 1312, a communicationinterface (I/F) 1314, and data storage 1316. The sheet sensor 1310 isconfigured to detect passage of a sheet as the sheet is conveyed by aconveyance roller, and may correspond to the sensor 104 in FIG. 1A. Thecontrol engine 1312 is configured to control the overall operations ofthe sheet processing apparatus 1304. An example of a detailed operationof the control engine 1312 is described below with reference to FIG. 14.In the example of the system shown in FIG. 13, the control engine 1312may include one or more processors, one or more storage devices, and/orother components. In some embodiments, the processors are programmed byone or more computer program instructions stored on the storage device.As used herein, for convenience, the various applicable instructionmodules will be described as performing an operation, when, in fact,various applicable instructions program the processors to perform thevarious applicable operations. The communication interface (I/F) 1314 isconfigured to perform communication with external devices based oncommands from the control engine 1312.

The data storage 1316 is configured to store sheet-conveyance datasets,such as individual sheet-conveyance datasets and summarysheet-conveyance datasets. The data storage 1316 includes an individualdata section 1318 for storing the individual sheet-conveyance datasets,and a summary data section 1320 for storing the summary sheet-conveyancedatasets. The data storage 1316 may be a detachable storage device, suchas a flash memory card (e.g., SD card), a USB memory stick, an externalhard disk drive (HDD), and other applicable external storage devices.

In the example of the system shown in FIG. 13, the management server1306 is one or more computer systems configured to manage operations ofthe sheet processing apparatus 1304, training system 1340, and/or sheetclassification system 1350. The management server 1306 may correspond tothe server apparatus 902 in FIG. 9. Specifically, the management server1306 includes a communication interface (I/F) 1322, a management engine1324, and summary data storage 1326. The communication interface (I/F)1322 is configured to perform communication with external devices basedon commands from the management engine 1324. The management engine 1324is configured to control the overall operations of the management server1306, and may have the same or similar configuration as the controlengine 1312 of the sheet processing apparatus 1304. The summary datastorage 1326 is configured to store summary sheet-conveyance datasets,which may be received from the sheet processing apparatus 1304. Anexample of a detailed operation of the management server 1306 isdescribed below with reference to FIG. 15. In some embodiments, themanagement server 1306 includes some or all of the functionality of thetraining system 1340 and/or sheet classification system 1350.

In the example of the system shown in FIG. 13, the client device 1308 isa computing device configured to receive communication regarding thesheet processing apparatus 1304 from the sheet processing apparatus 1304and/or the management server 1306. In some embodiments, the clientdevice 1308 may correspond to the mobile computing device 903 in FIG. 9.Specifically, the client device 1308 includes a communication interface(I/F) 1328, a processing engine 1330, and an input/output engine 1332.The communication interface (I/F) 1328 is configured to performcommunication with external devices based on commands from theprocessing engine 1330. The processing engine 1330 is configured tocontrol the overall operations of the client device 1308, and may havethe same or similar configuration as the control engine 1312 of thesheet processing apparatus 1304 and/or the management engine 1324 of themanagement server 1306. The input/output engine 1332 is configured tomanage inputs from and outputs to local devices/modules connected to theclient device 1308, such as a display device, a physical or virtualkeyboard, and a speaker, etc. An example of a detailed operation of theclient device 1308 is described below with reference to FIG. 16.

In the example of the network system 1300 shown in FIG. 13, the trainingsystem 1340 is configured to generate training data 1342 and/or train amachine learning model 1347 for classifying sheets (e.g., sheets ofpaper). As shown, the training system 1340 includes a training engine1342, a communication interface (I/F) 1344, and a data storage 1346. Inone example, the training engine 1342 generates training data 1348 usingone or more test devices (e.g., a sheet processing apparatus and/orother image forming apparatus) in a test environment. The training data1348 may be used as input for the machine learning model, such as aK-Nearest Neighbor (KNN) model, logistic regression model, and/or othersupervised machine learning model). The data storage 1340 may store thegenerated training dataset 1342 and the machine learning model(s) 1347.The communication interface (I/F) 1344 may be configured to performcommunication with external devices based on commands from the trainingengine 1342.

In some embodiments, K-nearest neighbors and logistic regression areexamples of supervised learning techniques used in classification thatmay be implemented by the machine learning model 1347. Generally, insupervised learning, a training data set is labeled (e.g., “premium” or“economy”). The machine learning model determines the label of an unseeninstance based on labeled training data. K nearest neighbors may usetraining data to classify new instances based on a similarity measureand/or threshold.

In some embodiments, Number of Neighbors (K) is a parameter used in aKNN classifier. The classifier may assign a classification label whichis most frequent among the K training samples nearest to that querypoint (e.g., distance functions). It may be a majority vote.

In some embodiments, logistic regression is a statistical model thatclassifies unseen cases into two possible values (or, binary outcomes),such 0/1, pass/fail, A/B, and/or the like. In some of the examplespresented herein, “economy” or “premium” are the possible values. Theclassifier may be a logistic function. In the example machine learningtechniques described elsewhere herein, K-Nearest Neighbors is used,however logistic regression may also be appropriate when there are twocategories are used for classification.

In the example of the network system 1300 shown in FIG. 13, the sheetclassification system 1350 is configured to classify sheets (e.g.,sheets of paper). As shown, the sheet classification system 1350includes a classification engine 1352, an adjustment engine 1354, acommunication interface (I/F) 1356, and a data storage 1358. The sheetclassification engine 1352 may utilize a machine learning model 1364 topredict a classification (e.g., economy paper, premium paper), andclassify the sheets according to the prediction. For example, theclassification engine 1352 may come pre-loaded (e.g., duringmanufacturing) with the training dataset 1348 and/or machine learningmodel 1364, and the machine learning model 1364 may use the trainingdataset 1348 as input to predict classification(s) for sheets passingthrough a deployed device (e.g., a deployed sheet processing apparatus1304 and/or other image forming apparatus).

The adjustment engine 1354 may be configured to adjust operationalparameters of a deployed device based on the predicted sheetclassification. For example, the adjustment engine 1354 may adjust imagequality, enhance toner usage, prolong life of a components (e.g.,printer cartridges, rollers, optical devices, and/or the like). Thecommunication interface (I/F) 1350 may be configured to performcommunication with external devices based on commands from theclassification engine 1352 and/or adjustment engine 1354.

It should be appreciated that although the various instructions areillustrated in FIG. 13 as being co-located within a single processingunit, in implementations in which processor(s) includes multipleprocessing units, one or more instructions may be executed remotely fromthe other instructions. Additionally, the modular software breakdown asillustrated in FIG. 13 is prepared for illustrative purposes only. Thevarious instructions described with respect to specific software modulesmay be implemented by alternative software modules configured indifferent arrangements and with alternative function sets.

The description of the functionality provided by the differentinstructions described herein is for illustrative purposes, and is notintended to be limiting, as any of instructions may provide more or lessfunctionality than is described. For example, one or more of theinstructions may be eliminated, and some or all of its functionality maybe provided by other ones of the instructions. As another example,processor(s) may be programmed by one or more additional instructionsthat may perform some or all of the functionality attributed herein toone of the instructions.

The various instructions described herein may be stored in a storagedevice, which may comprise random access memory (RAM), read only memory(ROM), and/or other memory. The storage device may store the computerprogram instructions (e.g., the aforementioned instructions) to beexecuted by the processor(s) as well as data that may be manipulated bythe processor(s). The storage device may comprise floppy disks, harddisks, optical disks, tapes, or other storage media for storingcomputer-executable instructions and/or data.

FIGS. 14A and 14B illustrate a flowchart 1400A and 1400B of an exampleof a method for operating a sheet processing apparatus according to someembodiments. An applicable engine for operating a sheet processingapparatus, such as the control engine 1312 of the sheet processingapparatus 1304 in FIG. 13, can perform steps of the flowchart 1400. Theflowchart 1400A includes a flow of steps 1402 to 1412 (first flow), theflowchart 1440B includes a flow of steps 1414 to 1420 (second flow) inparallel. Depending on the specific implementation, the two flows may becarried out in series or in an interleaved manner. The first flow of theflowchart 1400A begins at step 1402 with calculating an individualsheet-conveyance dataset, upon sheet detection of a sheet. In someembodiments, the individual sheet-conveyance dataset may indicate asheet passage interval corresponding to the sheet, and may be stored inan applicable section of data storage such as the individual datasection 1318 in the data storage 1316 in FIG. 13. For example, anindividual sheet-conveyance dataset may solely include a datapointrepresenting a sheet passage interval. In another example, an individualsheet-conveyance dataset may include a datapoint representing a sheetpassage interval, a datapoint representing a sheet identificationnumber, and a datapoint representing the current time (e.g., time ofgenerating the individual sheet-conveyance dataset). Here, a “datapoint”may refer to a certain metric value, and a “dataset” may refer to one ormore datapoints. Each time an individual sheet-conveyance dataset iscalculated upon sheet detection, a counter is incremented to indicatethe number of individual sheet-conveyance datasets that have beencalculated.

The first flow of the flowchart 1400A continues to step 1404 withdetermining whether or not a counted number of individualsheet-conveyance datasets reached a threshold number. In someembodiments, the threshold number is variably set, and a defaultthreshold number may be set. For example, the default threshold numberis 100, and the threshold number may be increased or decreased dependingon sheet conveyance performance statics calculated as described below.When it is determined that the counted number of individualsheet-conveyance datasets reached the threshold number (Yes in step1404), the process proceeds to step 1406; otherwise, the process returnsto step 1402.

The first flow of the flowchart 1400A continues to step 1406 withcalculating a summary sheet-conveyance dataset based on the thresholdnumber of individual sheet-conveyance datasets. In some embodiments, thesummary sheet-conveyance dataset may include sheet passage intervalstatistics, such as the predetermined number, an average sheet passageinterval of the predetermined number, a standard deviation of the sheetpassage intervals of the predetermined number, a 25 percentile sheetpassage interval, a 75 percentile sheet passage interval, the number ofsheet passage intervals that exceeded an upper threshold interval, anaverage of sheet passage intervals that exceeded the upper thresholdinterval, the number of sheet passage intervals below a lower thresholdinterval, and an average of sheet passage intervals below the lowerthreshold interval. In some embodiments, the upper threshold intervaland/or the lower threshold interval may be determined according to atype of a corresponding sheet conveyance roller, and may be adjusteddepending on various applicable criteria, such as the average sheetpassage interval. For example, a summary sheet-conveyance dataset mayinclude one or more of the sheet passage interval statistics listedabove. In another example, a summary sheet-conveyance dataset mayinclude one or more of the sheet passage interval statistics listedabove and/or individual sheet-conveyance datasets of some of thecorresponding sheets, such as individual sheet-conveyance datasets withlarge deviation from the average sheet passage interval.

In some embodiments, a data size of the summary sheet-conveyance datasetmay be smaller than a data size of the individual sheet-conveyancedatasets based on which the summary sheet-conveyance dataset wascalculated. To reduce the data size of the summary sheet-conveyancedataset, the summary sheet-conveyance dataset may be compressed using anapplicable data compression algorithm. In some embodiments, noisereduction is carried out with respect to the summary sheet-conveyancedataset. For example, when there is structural data artifacts, such asshift in data for a limited duration of time, the problematic data isshifted back to an expected position.

The first flow of the flowchart 1400A continues to step 1408 withstoring the summary sheet-conveyance dataset, discarding individualsheet-conveyance datasets based on which the summary sheet-conveyancedataset was calculated, and clearing the counted number. In someembodiments, the summary sheet-conveyance dataset is stored in anapplicable section of data storage such as the summary 1320 in the datastorage 1316 in FIG. 13. “Discard” here may or may not involvepermanently deleting data. By storing the summary sheet-conveyancedataset and discarding the individual sheet-conveyance datasets, it ispossible to save storage space for sheet-conveyance datasets.

The first flow of the flowchart 1400A continues to step 1410 withdetermining whether or not the summary sheet-conveyance dataset meetscriteria to modify the predetermined number. In some embodiments, thecriteria to modify the predetermined number may include an event whenthe sheet passage interval statistics indicate that the number of sheetsconveyed abnormally slowly or quickly among the predetermined number ofsheets is greater than a first threshold value (e.g., 5% of thepredetermined number). In some embodiments, the criteria to modify thepredetermined number may include an event when the sheet passageinterval statistics indicate that the number of sheets conveyedabnormally slowly or quickly among the predetermined number of sheets isless than a second threshold value (e.g., 0). The second threshold valuemay be smaller than the first threshold value. Depending on a specificimplementation, the first and/or the second threshold values may befixed or variable. When it is determined that the summarysheet-conveyance dataset meets the criteria to modify the predeterminednumber (Yes in step 1410), the process proceeds to step 1412; otherwisethe process returns to step 1402.

The first flow of the flowchart 1400A continues to step 1412 withmodifying the predetermined number. In some embodiments, thepredetermined number is decreased when the sheet passage intervalstatistics indicate that the number of sheets conveyed abnormally slowlyor quickly among the predetermined number of sheets is greater than thefirst threshold value. In some embodiments, the predetermined number isincreased when the sheet passage interval statistics indicate that thenumber of sheets conveyed abnormally slowly or quickly among thepredetermined number of sheets is less than the second threshold value.In some embodiments, the predetermined number may be decreased by afirst predetermined ratio (e.g., 50%), and increased by a secondpredetermined ratio. For example, for an original predetermined numberof 100 and a first predetermined ratio of 50%, the increasedpredetermined number may be raised from 100 to 150. The firstpredetermined ratio may be equal to the second predetermined ratio. Insome embodiments, the predetermined number may be decreased by a firstpredetermined number (e.g., 100), and increased by a secondpredetermined number. For example, for an original predetermined numberof 100 and a first predetermined number of 100, the increasedpredetermined number may be raised from 100 to 200. The firstpredetermined number may be equal to the second predetermined number.The upper limit and/or the lower limit of the predetermined number maybe set. After step 1412, the process returns to step 1402.

The second flow of the flowchart 1400B continues to step 1414 withdetermining whether or not a transmission trigger event is detected. Insome embodiments, the transmission trigger event includes at least oneof an event when a predetermined scheduled time (e.g., 0:00 AM) isreached, an event when a user instruction is received, and an event whena control signal to notify an error is generated. Depending on aspecific implementation, the predetermined scheduled time may be set bya user. The control signal may be generated, for example, when a sheetjam and/or a sheet conveyance anomaly is detected. When it is determinedthat the transmission trigger event is detected (Yes in step 1414), theprocess proceeds to step 1416;otherwise the process returns to step1414.

The second flow of the flowchart 1400B continues to step 1416 withtransmitting summary sheet-conveyance dataset(s) stored in data storage.In some embodiments, all summary sheet-conveyance datasets stored indata storage, such as the summary data section 1320 of the data storage1316 in FIG. 13, may be transmitted. In some embodiments, one or moresummary sheet-conveyance datasets that are stored in the storage andhave not been transmitted may be transmitted. The summarysheet-conveyance dataset may be transmitted to a management server, suchas the management server 1306 in FIG. 13, and/or to a client device,such as the client device 1308 in FIG. 13.

The second flow of the flowchart 1400B continues to step 1418 withdetermining whether or not a reset trigger event is detected. In someembodiments, the reset trigger event may include an event when aparticular roller of a sheet processing apparatus is replaced with areplacement roller. When it is determined that the reset trigger eventis detected (Yes in step 1418), the process proceeds to step 1420;otherwise the process returns to step 1414.

The second flow of the flowchart 1400B continues to step 1420 withperforming a clear operation to clear sheet-conveyance datasets storedin data storage. In some embodiments, the clear operation involvesclearing individual sheet-conveyance datasets stored in data storage,and/or clearing summary sheet-conveyance datasets stored in datastorage. Depending on a specific implementation, clearing may or may notinvolve permanently deleting data. After step 1420, the process returnsto step 1414.

FIG. 15 illustrates a flowchart 1500 of an example of a method foroperating a management server according to some embodiments. Anapplicable engine for operating a management server, such as themanagement engine 1324 of the management server 1306 in FIG. 13, canperform steps of the flowchart 1500. The flowchart 1500 begins at step1502 with receiving one or more summary sheet-conveyance datasets from asheet processing apparatus. The received summary sheet-conveyancedataset(s) may be stored in applicable data storage, such as the summarydata storage 1326 in FIG. 13.

The flowchart 1500 continues to step 1504 with performing an analysis ofthe received summary sheet-conveyance dataset(s). In some embodiments,the analysis of the received summary sheet-conveyance dataset(s) iscarried out to detect sheet conveyance anomalies. For example, theanalysis of the received summary sheet-conveyance dataset(s) may includeanalysis of one or more datapoints included in the received summarysheet-conveyance dataset(s), such as the standard deviation, the 25percentile sheet passage interval, and the 75 percentile sheet passageinterval, to detect sheet conveyance anomalies. In detecting sheetconveyance anomalies, the one or more datapoints may be compared withreference datapoints (standard datapoints) representing a normal sheetconveyance state.

The flowchart 1500 continues to step 1506 with determining whether ornot an alert trigger condition is met. In some embodiments, the alerttrigger condition may include an event when the number of sheetsconveyed abnormally slowly or quickly is greater than a threshold value.For example, a number of sheet passage intervals lower than the 25percentile sheet passage interval may indicate abnormally quick sheetconveyance, and a number of sheet passage intervals higher than the 75percentile sheet passage interval may indicate abnormally slow sheetconveyance. When it is determined that the alert trigger condition ismet (Yes in step 1506), the process proceeds to step 1508; otherwise theprocess returns to step 1502.

The flowchart 1500 continues to step 1508 with generating andtransmitting a sheet conveyance anomaly alert. In some embodiments, thesheet conveyance anomaly alert may be transmitted to a correspondingsheet processing apparatus and/or a client device associated with thecorresponding sheet processing apparatus.

The flowchart 1500 continues to step 1510 with determining whether ornot a summary analysis meets criteria to modify a predetermined number.In some embodiments, the criteria to modify the predetermined number mayor may not be the same as the criteria to modify the predeterminednumber employed in a sheet processing apparatus, as those in step 1410in FIG. 14. In some embodiments, the determination whether to modify thepredetermined number may be performed only by the management server.

The flowchart 1500 continues to step 1512 with transmitting a command tomodify the predetermined number to a sheet processing apparatus. In someembodiments, the command causes the sheet processing apparatus thatreceived the command to modify the predetermined number to triggersummary of individual sheet-conveyance datasets. After step 1512, theprocess returns to step 1502 for newly received summary sheet-conveyancedataset(s).

FIG. 16 illustrates a flowchart 1600 of an example of a method foroperating a client device according to some embodiments. An applicableengine for operating a management server, such as the processing engine1330 of the client device 1308 in FIG. 13, can perform steps of theflowchart 1600. The flowchart 1600 begins at step 1602 with presenting asheet conveyance anomaly alert message, in response to a sheetconveyance anomaly alert. In some embodiments, the sheet conveyanceanomaly alert message may be displayed on a screen of a display deviceincorporated in or connected to a client device. For example, a messagemay indicate that a specific sheet conveyance roller (e.g., identifiedby a location) of a specific sheet processing apparatus (e.g.,identified by an ID) may be causing a sheet conveyance anomaly. Themessage may further include a specific instruction or recommendation tohandle the sheet conveyance anomaly, such as alignment adjustment orreplacement of the roller.

The flowchart 1600 continues to step 1604 with receiving a user input tohandle the sheet conveyance anomaly. In some embodiments, the user inputis received by an applicable engine such as the input/output engine 1332in FIG. 13. For example, a user input may be made using physical inputdevices, such as a keyboard, a mouse, a microphone, and so on. Inanother example, a user input may be made using virtual input modules,such as touch inputs on a virtual user interface on a display.

The flowchart 1600 continues to step 1606 with transmitting a requestcorresponding to the user input. In some embodiments, the request istransmitted from an applicable module such as the communicationinterface 1328 in FIG. 13 to an applicable apparatus, such as the sheetprocessing apparatus 1304 and/or the management server 1306 in FIG. 13.For example, the request may include a command to stop operations of asheet processing apparatus, and/or a command to redirect sheetprocessing commands (e.g., print commands) to another sheet processingdevice. In another example, the request may include aservice/maintenance request and/or a purchase request to purchase areplacement roller.

FIG. 17 illustrates a flowchart 1700 of an example method of classifyingsheets according to some embodiments. In this and other flowcharts, theflowchart 1700 illustrates by way of example a sequence of steps. Itshould be understood the steps may be reorganized for parallelexecution, or reordered, as applicable. Moreover, for the sake ofclarity, some steps that could have been included may have been excludedand some steps that could have been excluded may have been included.

In step 1702, a training system (e.g., training system 1340) generates atraining dataset (e.g., training dataset 1348). In some embodiments, atraining engine (e.g., training engine 1342) generates the trainingdataset. Generally, during printer manufacturing, e.g., while testingand evaluating, engineers may test sheet processing apparatus by runningsimulations with various types of sheets. The test sheet processingapparatus may collect various technical data such as calibration timing,voltage, fuser temperature, and/or the like. This information may bestored in memory and/or written to a log file.

In some embodiments, the test sheet processing apparatus may alsocollect sheet timing data. The sheet timing data may include the traveltime (e.g., in milliseconds) it takes a sheet to travel from point A topoint B within the test sheet processing apparatus (e.g., across asensor). The sheet travel time may depend on the condition of therollers inside the printer, the quality of paper, moisture in the air,temperature, and/or the like. Example travel time data using premiumpaper is shown in FIG. 19.

In some embodiments, the training system may aggregate the sheet timedata for each page interval and may calculate summary statistics (e.g.,Mean, Standard Deviation, Minimum, Maximum, Percentiles, etc.).

In some embodiments, the test engineers can classify the sheet beingused, and can enter sheet identifiers into the training system. In oneexample, sheets may be classified into “Economy” or “Premium”. Suchclassification may be used to associate the training data with the sheetclassification label (e.g., supervised learning requires labels intraining data).

An example summary of statistics is shown in FIG. 20. Although there areonly two sheet classifications (e.g., Premium and Economy) in thisexample, sheets may be classified into as several classifications. Forexample, sheets may be classified as “Premium”, “Economy”, “Glossyphoto”, and “Recycled paper”. Sheet classifications may be associatedwith corresponding sheet classification labels. For example, a sheet maybe classified as “Premium,” and be associated (e.g., assigned, labeled)a “Premium” sheet classification label.

FIG. 20 shows sample data from a single test sheet processing apparatus.The test engineers may run simulations with many test sheet processingapparatus, so similar datasets may be available from each machine. Thetraining data from each machine may be combined, averaged, summarized,and/or the like. The summary statistics with the sheet classificationsfrom multiple test sheet processing apparatus may be included in theinitial training dataset (e.g., training dataset 1348). The combinedtraining dataset can be loaded into a production device's memory (e.g.,memory of a sheet processing apparatus 1304), in its operating system orfirmware, or uploaded to a remote server to which the machine is to beconnected. Accordingly, the initial training data is available locally,in the remote server and/or in another remote device.

The training dataset may use one or more feature dimensions. The exampletraining dataset of FIG. 20 shows five feature dimensions: Mean,Standard Deviation (SD), Median, Max, and Min. However, the machinesherein may use fewer, more or different feature dimensions. In someembodiments, the page index and at least one feature dimension may beused to make a meaningful classification. In some embodiments,classification may start with one dimension and may increase the numberof dimensions in steps, e.g., if the accuracy is poor.

In some embodiments, as long as training classifications (labels) areprovided, data other than the summary statistics can be used as inputusing the same process.

In step 1704, a sheet classification system (e.g., sheet classificationsystem 1350) obtains the training dataset (e.g., training dataset 1348).For example, the training dataset may have been pre-loaded onto thesheet classification system. In some embodiments, the sheetclassification system may comprise a portion of a deployed sheetprocessing apparatus (e.g., sheet processing apparatus 1304) and/orother deployed image forming apparatus. The training dataset may, forexample, be preloaded during manufacturing. In some embodiments, thesheet classification system may be part of a remote device, such as aremote server or other remote device, and may communicate with the sheetclassification system over a communication network (e.g., communicationnetwork 1302).

In step 1706, the sheet classification system obtains sheet timing data(e.g., sheet timing data 1360). The sheet classification system mayobtain the sheet timing data from a sheet processing apparatus (e.g.,sheet processing apparatus 1304).

In some embodiments, the sheet processing apparatus may collect and/ormodify the sheet timing data during “real-world” use of the sheetprocessing apparatus, and provide the sheet timing data to the sheetclassification system in real-time. The sheet timing data may compriseand/or be based on information of the stored individual data section(e.g., individual data section 1318), summary data section, and/or otherinformation generated and/or obtained by the sheet processing apparatus.

In step 1708, the sheet processing apparatus may calculate summarystatistics for one or more interval windows. Each interval window mayinclude a predetermined number of sheets (e.g., as shown in FIGS. 19 and20).

In step 1710, the sheet classification system using a machine learningmodel (e.g., machine learning model 1347) to classify sheets. Forexample, the machine learning model may use K-Nearest Neighbors orLogistic Regression to classify sheets for each x-page window based onthe initial training dataset. An example algorithm implemented by themachine learning model may use five feature dimensions (Mean, SD,Median, Max, Min), and the training data may have two classificationlabels (e.g., premium and economy). When there are more than two labels,the machine learning model may use K-Nearest Neighbors instead oflogistic regression because the logistic regression classifiersgenerally make binary classifications.

In some embodiments, the summary statistics for a particular sheetrange, the n-th window, is represented as a pair (Mean, SD, Median, Max,Min).

For example (318.2, 5.226854, 318, 332, 292). Let's call it p_(n).

p ₁₀₀=(318.2, 5.226854, 318, 332, 292)

The KNN parameter may be K=3. This may be the number of neighbors.

The KNN classifier may find three closest pairs to p₁₀₀=(318.2,5.226854, 318, 332, 292) among the training data set for the 100thwindow using the Euclidian distance metric.

Given a pair in the training set q=(q₁, q₂, q₃, q₄, q₅), the distancebetween p and q is calculated by:

d=√{(318.2−q ₁)²+(5.226854−q ₂)²+(318−q ₃)²+(332−q ₄)²+(292−q ₅)²}

By majority vote, the classifier may classify the 100th window intopremium or economy sheets. An example classification of the 100^(th)window is shown in FIG. 23.

In some embodiments, the classification may be performed locally (e.g.,if the sheet classification system is part of the sheet processingapparatus) by a classification engine (e.g., classification engine1352). In some embodiments, the classification may be performed remotely(e.g., if the sheet classification system is part of a remote server),and the summary information from step 1708 may have been uploaded to theremote server.

In step 1712, the sheet classification system may create and/or obtainadditional training data (e.g., additional training data 1349). Althoughthe machine may have the initial training data described above, the useror a technical dispatcher can create additional training data (e.g.,additional training data 1349) that is specific to the user. To do so,the user or the dispatcher may provide sheet classification to themachine.

In some embodiments, the specific training data generated at acustomer's site may be specific to the customer usage. In addition, theuser may use custom classifications that are not used in the initialtraining data from the lab. For example, the user can use specificclassifications (labels) such as “glossy photo paper,” “textured photopaper,” “inkjet,” and/or the like. In some embodiments, theclassification engine creates and/or obtains the additional trainingdata.

In step 1714, the sheet classification system updates the originaltraining data and/or switches to the additional training data to performclassification (e.g., instead of or in addition to the trainingdataset). For example, the update and/or switch may occur once athreshold amount of additional training data and/or sheet timing datahas been collected. In some embodiments, the classification engineperforms the update and/or switch.

In step 1716, in response to a sheet classification, the sheetclassification system may adjust one or more operational parameters ofthe sheet processing apparatus. In some embodiments, the adjustmentengine (e.g., adjustment engine 1354) performs the adjustment.

In step 1718, the sheet classification system evaluates accuracy of theclassifications. For example, to evaluate the accuracy of sheetclassifications, the user and/or technical dispatcher may enter theactual sheet classification into the printer's system. Theclassification engine may compare the predicted classification with theactual classification and may calculate prediction accuracy. An exampletable in FIG. 21 shows predicted and actual sheet types on the right twocolumns. In FIG. 21, the classification for the 561th-562th windows wereincorrect.

If the classification engine determines accuracy is poor, theclassification engine can attempt to improve accuracy by, for example,performing one or more of the following:

-   -   Increasing the number of feature dimensions;    -   Switching the training dataset from the original training        dataset to the additional training data, if available;    -   Updating the original training dataset with the additional        training dataset; and/or    -   If KNN is used, changing the number of neighbors (e.g.,        parameter K). If KNN does not improve accuracy, one or more        other machine learning techniques may be used.

Steps 1716 and/or 1718 may be repeated until a desired level of accuracyis obtained.

FIG. 18 illustrates a flowchart 1800 of an example method of classifyingsheets, according to some embodiments.

In step 1802, a sheet classification system (e.g., sheet classificationsystem 1350) obtains a training dataset (e.g., training dataset 1348).The training dataset may be obtained by a communication interface (e.g.,communication interface (I/F) 1356) from a training system (e.g.,training system 1340). The training dataset may come pre-loaded, alongwith a machine learning model (e.g., machine learning model 1347). Thetraining dataset may comprise sheet passage interval informationassociated with conveyance of a plurality of sheets in a sheetconveyance path of one or more test sheet processing apparatus. At leasta portion of the sheets of the plurality of sheets may have a knownsheet classification (e.g., economy) of a set of sheet classifications(e.g., economy and premium). The training dataset may be generated by atraining engine (e.g., training engine 1342) of a training system (e.g.,training system 1340).

In step 1804, the sheet classification system obtains sheet timing dataassociated with the classifying. The sheet timing data may comprisesheet passage interval information associated with conveyance of thepredetermined number of sheets in the sheet conveyance path of thedeployed sheet processing apparatus. In some embodiments, thecommunication interface (I/F) obtains the sheet timing data from thedeployed sheet processing apparatus.

In step 1806, the sheet classification system classifies, using amachine learning model, the training dataset and the sheet timing data,for each interval window of a plurality of different interval windows, apredetermined number of sheets in a sheet conveyance path of thedeployed sheet processing apparatus as a particular classification ofthe set of sheet classifications. In some embodiments, a classificationengine (e.g., classification engine 1352) performs the classification.

In step 1808, the sheet classification system adjusts, based on theclassifying, one or more operational parameters of deployed sheetprocessing apparatus. In some embodiments, an adjustment engine (e.g.,adjustment engine 1354) performs the adjustment.

In some embodiments, the sheet classification system comprises a portionof the deployed sheet processing apparatus. In some embodiments, thesheet classification system comprises at least a portion of a remoteserver coupled to the deployed sheet processing apparatus over acommunication network. In some embodiments, the one or more operationalparameters of the deployed sheet processing apparatus include any ofimage quality and toner usage.

In some embodiments, the sheet classification system may also obtainadditional training data (e.g., additional training data 1349)associated with the deployed sheet processing apparatus, switch theclassifying from using the original training dataset to using theadditional training data (notably, in some embodiments, the additionaltraining data may include information from the original trainingdataset), and/or classify, using the machine learning model and theadditional training data, for each interval window of a second pluralityof different second interval windows, a second predetermined number ofsheets in the sheet conveyance path of the deployed sheet processingapparatus as a second classification of a second set of sheetclassifications. In some embodiments, the updating of the originaltraining data and/or switch from the original training data to theadditional training data may be performed based on obtaining a thresholdamount of the sheet timing data. In some embodiments, the second set ofsheet classifications includes at least a portion of the set of sheetclassifications and at least one additional sheet classificationreceived by the deployed sheet processing apparatus.

In some embodiments, the sheet classification system may obtain theadditional training data associated with the deployed sheet processingapparatus. The sheet classification system may switch the classifyingfrom using the original training dataset alone to using the originaltraining dataset and the additional training data, and may use themachine learning model to perform classifications for each intervalwindow of a second plurality of different second interval windows, asecond predetermined number of sheets in the sheet conveyance path ofthe deployed sheet processing apparatus as a second classification of asecond set of sheet classifications.

In some embodiments, the sheet classification system may iterativelymodify any of a type or number of inputs to the machine learning modeluntil a threshold accuracy level is achieved.

In some embodiments, the sheet classification system may also evaluateaccuracy of the sheet classification and/or optimize classificationperformance. For example, the sheet classification system may receiveactual sheet classification (e.g., a user indicating an actual sheetclassification of a sheet) for a particular interval window of theplurality of interval windows. The sheet classification system maycompare the actual sheet classification with the determined sheetclassification associated with the particular interval window of theplurality of interval windows, and determine classification accuracybased on the comparison.

FIG. 20 illustrates a table 2000 of example summary statistics withsheet classification labels according to some embodiments. In theexample of FIG. 20, the summary statistics are for every 100 pages ofeconomy paper (top) and premium paper (below) from Machine 1 withclassification labels. The index of 1 means that the row includes theaggregation of the 1st to the 100th pages.

FIG. 22 illustrates a diagram of an example pipeline 2200 for sheetclassification according to some embodiments. As shown, manufacturingmay perform lab simulations to generate training data (e.g., trainingdataset 1348) and/or a machine learning model (e.g., machine learningmodel 1347). The training data may include a combination of a summarystatistics and classification label(s) for each page window. Thetraining data may be provided to a user site or loaded onto a sheetprocessing apparatus 1304 (before or after deployment). The sheetprocessing apparatus may collect additional timing data and/or obtainadditional classification from users to generate additional trainingdata (e.g., additional training data 1349). The sheet classificationsystem (e.g., sheet classification system 1350) may classify sheetsbased on the machine learning model. As stated herein, the sheetclassification system may be part of the sheet processing apparatusand/or a remote server.

Hardware Implementation

The techniques described herein can be implemented using one or morespecial-purpose computing devices. The special-purpose computing devicesmay be hard-wired to perform the techniques, or may include circuitry ordigital electronic devices such as one or more application-specificintegrated circuits (ASICs) or field programmable gate arrays (FPGAs)that are persistently programmed to perform the techniques, or mayinclude one or more hardware processors programmed to perform thetechniques pursuant to program instructions in firmware, memory, otherstorage, or a combination. Such special-purpose computing devices mayalso combine custom hard-wired logic, ASICs, or FPGAs with customprogramming to accomplish the techniques. The special-purpose computingdevices may be desktop computer systems, server computer systems,portable computer systems, handheld devices, networking devices or anyother device or combination of devices that incorporate hard-wiredand/or program logic to implement the techniques.

Computing device(s) are generally controlled and coordinated byoperating system software, such as iOS, Android, Chrome OS, Windows XP,Windows Vista, Windows 7, Windows 8, Windows 10, Windows Server, WindowsCE, Unix, Linux, SunOS, Solaris, iOS, Blackberry OS, VxWorks, or othercompatible operating systems. In other embodiments, the computing devicemay be controlled by a proprietary operating system. Conventionaloperating systems control and schedule computer processes for execution,perform memory management, provide file system, networking, I/Oservices, and provide a user interface functionality, such as agraphical user interface (“GUI”), among other things.

FIG. 24 illustrates a block diagram that illustrates a computer system2400 upon which computer-based processing involved in embodimentsdescribed herein may be implemented. In some embodiments, the computersystem 2400 can be employed as the controller 106 in FIG. 1A, a maincontrol module of the image forming apparatus 901, a main control moduleof the server apparatus 902, and/or a main control module of the mobilecomputing device 903 in FIG. 9. In some embodiments, the computer system2400 can be employed as one or more engines in FIG. 13. The computersystem 2400 includes a bus 2402 or other communication mechanism forcommunicating information, one or more hardware processors 2404 coupledwith bus 2402 for processing information. Hardware processor(s) 2404 maybe, for example, one or more general purpose microprocessors.

The computer system 2400 also includes a main memory 2406, such as arandom access memory (RAM), cache and/or other dynamic storage devices,coupled to bus 2402 for storing information and instructions to beexecuted by processor 2404. Main memory 2406 also may be used forstoring temporary variables or other intermediate information duringexecution of instructions to be executed by processor 2404. Suchinstructions, when stored in storage media accessible to processor 2404,render computer system 2400 into a special-purpose machine that iscustomized to perform the operations specified in the instructions.

The computer system 2400 further includes a read only memory (ROM) 2408or other static storage device coupled to bus 2402 for storing staticinformation and instructions for processor 2404. A storage device 2410,such as a magnetic disk, optical disk, or USB thumb drive (Flash drive),etc., is provided and coupled to bus 2402 for storing information andinstructions.

The computer system 2400 may be coupled via bus 2402 to a display 2412,such as a cathode ray tube (CRT) or LCD display (or touch screen), fordisplaying information to a computer user. An input device 2414,including alphanumeric and other keys, is coupled to bus 2402 forcommunicating information and command selections to processor 2404.Another type of user input device is cursor control 2416, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 2404 and for controllingcursor movement on display 2412. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Insome embodiments, the same direction information and command selectionsas cursor control may be implemented via receiving touches on a touchscreen without a cursor.

The computing system 2400 may include a user interface module toimplement a GUI that may be stored in a mass storage device asexecutable software codes that are executed by the computing device(s).This and other modules may include, by way of example, components, suchas software components, object-oriented software components, classcomponents and task components, processes, functions, attributes,procedures, subroutines, segments of program code, drivers, firmware,microcode, circuitry, data, databases, data structures, tables, arrays,and variables.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Java, C or C++. A software module may becompiled and linked into an executable program, installed in a dynamiclink library, or may be written in an interpreted programming languagesuch as, for example, BASIC, Perl, or Python. It will be appreciatedthat software modules may be callable from other modules or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules configured for execution on computingdevices may be provided on a computer readable medium, such as a compactdisc, digital video disc, flash drive, magnetic disc, or any othertangible medium, or as a digital download (and may be originally storedin a compressed or installable format that requires installation,decompression or decryption prior to execution). Such software code maybe stored, partially or fully, on a memory device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules may be comprised of connectedlogic units, such as gates and flip-flops, and/or may be comprised ofprogrammable units, such as programmable gate arrays or processors. Themodules or computing device functionality described herein arepreferably implemented as software modules, but may be represented inhardware or firmware. Generally, the modules described herein refer tological modules that may be combined with other modules or divided intosub-modules despite their physical organization or storage.

The computer system 2400 may implement the techniques described hereinusing customized hard-wired logic, one or more ASICs or FPGAs, firmwareand/or program logic which in combination with the computer systemcauses or programs computer system 2400 to be a special-purpose machine.According to one embodiment, the techniques herein are performed bycomputer system 2400 in response to processor(s) 2404 executing one ormore sequences of one or more instructions contained in main memory2406. Such instructions may be read into main memory 2406 from anotherstorage medium, such as storage device 2410. Execution of the sequencesof instructions contained in main memory 2406 causes processor(s) 2404to perform the process steps described herein. In alternativeembodiments, hard-wired circuitry may be used in place of or incombination with software instructions.

The term “non-transitory media,” and similar terms, as used hereinrefers to any media that store data and/or instructions that cause amachine to operate in a specific fashion. Such non-transitory media maycomprise non-volatile media and/or volatile media. Non-volatile mediaincludes, for example, optical or magnetic disks, such as storage device2410. Volatile media includes dynamic memory, such as main memory 2406.Common forms of non-transitory media include, for example, a floppydisk, a flexible disk, hard disk, solid state drive, magnetic tape, orany other magnetic data storage medium, a CD-ROM, any other optical datastorage medium, any physical medium with patterns of holes, a RAM, aPROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge, and networked versions of the same.

Non-transitory media is distinct from but may be used in conjunctionwith transmission media. Transmission media participates in transferringinformation between non-transitory media. For example, transmissionmedia includes coaxial cables, copper wire and fiber optics, includingthe wires that comprise bus 2402. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 2404 for execution. Forexample, the instructions may initially be carried on a magnetic disk orsolid state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 2400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 2402. Bus 2402 carries the data tomain memory 2406, from which processor 2404 retrieves and executes theinstructions. The instructions received by main memory 2406 mayoptionally be stored on storage device 2410 either before or afterexecution by processor 2404.

The computer system 2400 also includes a network interface 2418 coupledto bus 2402. Network interface 2418 provides a two-way datacommunication coupling to one or more network links that are connectedto one or more local networks. For example, network interface 2418 maybe an integrated services digital network (ISDN) card, cable modem,satellite modem, or a modem to provide a data communication connectionto a corresponding type of telephone line. As another example, networkinterface 2418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN (or WAN component tocommunicate with a WAN). Wireless links may also be implemented. In anysuch implementation, network interface 2418 sends and receiveselectrical, electromagnetic or optical signals that carry digital datastreams representing various types of information.

A network link typically provides data communication through one or morenetworks to other data devices. For example, a network link may providea connection through a local network to a host computer or to dataequipment operated by an Internet Service Provider (ISP). The ISP inturn provides data communication services through the world wide packetdata communication network now commonly referred to as the “Internet”.Local network and Internet both use electrical, electromagnetic oroptical signals that carry digital data streams. The signals through thevarious networks and the signals on network link and throughcommunication interface 2418, which carry the digital data to and fromcomputer system 2400, are example forms of transmission media.

The computer system 2400 can send messages and receive data, includingprogram code, through the network(s), network link and network interface2418. In the Internet example, a server might transmit a requested codefor an application program through the Internet, the ISP, the localnetwork and the network interface 2418.

The received code may be executed by processor 2404 as it is received,and/or stored in storage device 2410, or other non-volatile storage forlater execution.

Each of the processes, methods, and algorithms described in thepreceding sections may be embodied in, and fully or partially automatedby, code modules executed by one or more computer systems or computerprocessors comprising computer hardware. The processes and algorithmsmay be implemented partially or wholly in application-specificcircuitry.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and sub-combinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Any process descriptions, elements, or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved, as would be understood by those skilled in the art.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure. The foregoing description details certainembodiments of the invention. It will be appreciated, however, that nomatter how detailed the foregoing appears in text, the invention can bepracticed in many ways. As is also stated above, it should be noted thatthe use of particular terminology when describing certain features oraspects of the invention should not be taken to imply that theterminology is being re-defined herein to be restricted to including anyspecific characteristics of the features or aspects of the inventionwith which that terminology is associated. The scope of the inventionshould therefore be construed in accordance with the appended claims andany equivalents thereof.

Engines, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, engines, or mechanisms. Engines may constitute eithersoftware engines (e.g., code embodied on a machine-readable medium) orhardware engines. A “hardware engine” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware engines ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware engine that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware engine may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware engine may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware engine may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware engine may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware enginemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwareengines become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware engine mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware engine” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented engine” refers to a hardware engine. Consideringembodiments in which hardware engines are temporarily configured (e.g.,programmed), each of the hardware engines need not be configured orinstantiated at any one instance in time. For example, where a hardwareengine comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware engines) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware engine at one instance oftime and to constitute a different hardware engine at a differentinstance of time.

Hardware engines can provide information to, and receive informationfrom, other hardware engines. Accordingly, the described hardwareengines may be regarded as being communicatively coupled. Where multiplehardware engines exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware engines. In embodiments inwhich multiple hardware engines are configured or instantiated atdifferent times, communications between such hardware engines may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware engines have access.For example, one hardware engine may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware engine may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware engines may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented enginesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented engine” refers to ahardware engine implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented engines. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented engines may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented engines may be distributed across a number ofgeographic locations.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the subject matter has been described withreference to specific example embodiments, various modifications andchanges may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the subject matter may be referred to herein, individually orcollectively, by the term “invention” merely for convenience and withoutintending to voluntarily limit the scope of this application to anysingle disclosure or concept if more than one is, in fact, disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

It will be appreciated that an “engine,” “system,” “data store,” and/or“database” may comprise software, hardware, firmware, and/or circuitry.In one example, one or more software programs comprising instructionscapable of being executable by a processor may perform one or more ofthe functions of the engines, data stores, databases, or systemsdescribed herein. In another example, circuitry may perform the same orsimilar functions. Alternative embodiments may comprise more, less, orfunctionally equivalent engines, systems, data stores, or databases, andstill be within the scope of present embodiments. For example, thefunctionality of the various systems, engines, data stores, and/ordatabases may be combined or divided differently.

“Open source” software is defined herein to be source code that allowsdistribution as source code as well as compiled form, with awell-publicized and indexed means of obtaining the source, optionallywith a license that allows modifications and derived works.

The data stores described herein may be any suitable structure (e.g., anactive database, a relational database, a self-referential database, atable, a matrix, an array, a flat file, a documented-oriented storagesystem, a non-relational No-SQL system, and the like), and may becloud-based or otherwise.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, engines, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

Although the invention has been described in detail for the purpose ofillustration based on what is currently considered to be the mostpractical and preferred implementations, it is to be understood thatsuch detail is solely for that purpose and that the invention is notlimited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present invention contemplates that, to theextent possible, one or more features of any embodiment can be combinedwith one or more features of any other embodiment.

1. A system comprising: one or more processors; and memory storinginstructions that, when executed by the one or more processors, causethe system to perform: obtaining a training dataset, the trainingdataset comprising sheet passage interval information associated withconveyance of a plurality of sheets in a sheet conveyance path of atraining sheet processing apparatus, at least a portion of the sheets ofthe plurality of sheets having a known sheet classification of a set ofsheet classifications; obtaining sheet timing data associated with theclassifying, the sheet timing data comprising sheet passage intervalinformation associated with conveyance of a predetermined number ofsheets in a sheet conveyance path of a deployed sheet processingapparatus; classifying, using a machine learning model, the trainingdataset and the sheet timing data, for an interval window of a pluralityof different interval windows, the predetermined number of sheets in thesheet conveyance path of the deployed sheet processing apparatus as aparticular classification of the set of sheet classifications; andadjusting, based on the classifying, one or more operational parametersof the deployed sheet processing apparatus.
 2. The system of claim 1,wherein the system comprises a portion of the deployed sheet processingapparatus.
 3. The system of claim 1, wherein the system comprises atleast a portion of a server system coupled over a communication networkto the deployed sheet processing apparatus.
 4. The system of claim 1,wherein the one or more operational parameters of the deployed sheetprocessing apparatus include any of image quality and toner usage. 5.The system of claim 1, wherein the instructions further cause the systemto perform: obtaining additional training data associated with thedeployed sheet processing apparatus; switching the classifying fromusing the training dataset to using second training data, the secondtraining data being based on the additional training data; andclassifying, using the machine learning model and the second trainingdata, for each interval window of a second plurality of different secondinterval windows, a second predetermined number of sheets in the sheetconveyance path of the deployed sheet processing apparatus as a secondclassification of a second set of sheet classifications.
 6. The systemof claim 5, wherein the switching is performed after obtaining athreshold amount of the sheet timing data.
 7. The system of claim 5,wherein the second set of sheet classifications includes at least aportion of the set of sheet classifications and at least one additionalsheet classification received by the deployed sheet processingapparatus.
 8. The system of claim 1, wherein the instructions furthercause the system to perform: receiving an actual sheet classificationfor a particular interval window of the plurality of different intervalwindows; comparing the actual sheet classification with the particularclassification associated with the particular interval window of theplurality of different interval windows; and determining, based on thecomparison, an accuracy of the classifying.
 9. The system of claim 8,wherein the instructions further cause the system to perform iterativelymodifying any of a type or number of inputs to the machine learningmodel until a threshold accuracy level is achieved.
 10. The system ofclaim 1, wherein the instructions further cause the system to perform:obtaining additional training data associated with the deployed sheetprocessing apparatus; generating second training data based on theadditional training data; and classifying, using the machine learningmodel, the second training data and the training dataset, for eachinterval window of a second plurality of different second intervalwindows, a second predetermined number of sheets in the sheet conveyancepath of the deployed sheet processing apparatus as a secondclassification of a second set of sheet classifications.
 11. A method,comprising: obtaining a training dataset, the training datasetcomprising sheet passage interval information associated with conveyanceof a plurality of sheets in a sheet conveyance path of a training sheetprocessing apparatus, at least a portion of the sheets of the pluralityof sheets having a known sheet classification of a set of sheetclassifications; obtaining sheet timing data associated with theclassifying, the sheet timing data comprising sheet passage intervalinformation associated with conveyance of a predetermined number ofsheets in a sheet conveyance path of a deployed sheet processingapparatus; classifying, using a machine learning model, the trainingdataset and the sheet timing data, for an interval window of a pluralityof different interval windows, the predetermined number of sheets in thesheet conveyance path of the deployed sheet processing apparatus as aparticular classification of the set of sheet classifications; andadjusting, based on the classifying, one or more operational parametersof the deployed sheet processing apparatus.
 12. The method of claim 11,wherein the deployed sheet processing apparatus is coupled to at least aportion of a remote server system over a communication network.
 13. Themethod of claim 11, wherein the one or more operational parameters ofthe deployed sheet processing apparatus include any of image quality andtoner usage.
 14. The method of claim 11, further comprising: obtainingadditional training data associated with the deployed sheet processingapparatus; switching the classifying from using the training dataset tousing second training data, the second training data being based on theadditional training data; and classifying, using the machine learningmodel and the second training data, for each interval window of a secondplurality of different second interval windows, a second predeterminednumber of sheets in the sheet conveyance path of the deployed sheetprocessing apparatus as a second classification of a second set of sheetclassifications.
 15. The method of claim 14, wherein the switching isperformed after obtaining a threshold amount of the sheet timing data.16. The method of claim 15, wherein the second set of sheetclassifications includes at least a portion of the set of sheetclassifications and at least one additional sheet classificationreceived by the deployed sheet processing apparatus.
 17. The method ofclaim 11, further comprising: receiving an actual sheet classificationfor a particular interval window of the plurality of different intervalwindows; comparing the actual sheet classification with the particularclassification associated with the particular interval window of theplurality of different interval windows; and determining, based on thecomparison, an accuracy of the classifying.
 18. The method of claim 17,further comprising modifying any of a type or number of inputs to themachine learning model until a threshold accuracy level is achieved. 19.The method of claim 11, further comprising: obtaining additionaltraining data associated with the deployed sheet processing apparatus;generating second training data based on the additional training data;and classifying, using the machine learning model, the second trainingdata and the training dataset, for each interval window of a secondplurality of different second interval windows, a second predeterminednumber of sheets in the sheet conveyance path of the deployed sheetprocessing apparatus as a second classification of a second set of sheetclassifications.